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相关概念视频

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Detection of Black Holes01:10

Detection of Black Holes

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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
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Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

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An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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相关实验视频

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基于GR-YOLO的密集行人检测

Nianfeng Li1, Xinlu Bai1, Xiangfeng Shen1

  • 1College of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, China.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
概括

本研究介绍了GR-yolo,这是一种改进的密集行人检测算法,增强了特征提取和多层次信息融合. 在拥挤的公共空间中,GR-yolo显著提高了检测准确度,提高了安全和保安.

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 密集的行人检测对于公共区域 (如机场和火车站) 的安全至关重要.
  • 当前的深度学习方法在特征提取,多尺度变化和高假阳性率方面扎.

研究的目的:

  • 提出一个改进的密集行人检测算法GR-yolo,基于Yolov8.8.
  • 为了增强特征提取,信息融合和密集行人场景的检测准确度.

主要方法:

  • 实现了GR-yolo,通过repc3模块优化骨干,以增强功能提取.
  • 使用聚合分布机制重建Yolov8部,以实现高效的多层次信息融合.
  • 利用Giou损失改善了对接和目标定位精度.

主要成果:

  • 与Yolov8相比,GR-yolo在多个数据集中表现出更高的性能.
  • 在Wider People上实现了3.1%的精度改进,在CrowdHuman上达到7.2%,在People检测图像上达到11.7%.
  • 在密集和多尺度的行人环境中成功降低了错过的检测率.

结论:

关键词:
这就是Yolov8的原因.通过行人检测系统检测行人.目标检测 目标检测 目标检测

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  • GR-yolo对于密集,多尺度和场景可变的行人检测是有效的.
  • 该算法为现实世界密集的行人检测挑战提供了一个有希望的方法.
  • 拟议的改进为改善公共安全应用中的行人检测系统提供了新的见解.